https://github.com/bearpaw/pyranet
Code for "Learning Feature Pyramids for Human Pose Estimation" (ICCV 2017)
https://github.com/bearpaw/pyranet
human-pose-estimation
Last synced: 8 months ago
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Code for "Learning Feature Pyramids for Human Pose Estimation" (ICCV 2017)
- Host: GitHub
- URL: https://github.com/bearpaw/pyranet
- Owner: bearpaw
- License: apache-2.0
- Created: 2017-07-18T07:18:17.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2021-12-20T20:18:32.000Z (over 4 years ago)
- Last Synced: 2025-03-30T22:22:05.493Z (about 1 year ago)
- Topics: human-pose-estimation
- Language: Lua
- Homepage:
- Size: 11 MB
- Stars: 222
- Watchers: 12
- Forks: 58
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Learning Feature Pyramids for Human Pose Estimation
Training and testing code for the paper
> **[Learning Feature Pyramids for Human Pose Estimation](https://arxiv.org/abs/1708.01101)**
> Wei Yang, Shuang Li, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
> ICCV, 2017
This code is based on [stacked hourglass networks](https://github.com/anewell/pose-hg-train) and [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). Thanks to the authors.
## Install
1. Install [Torch](http://torch.ch/).
2. Install dependencies.
```
luarocks install hdf5
luarocks install matio
luarocks install optnet
```
3. (Optional) Install nccl for better performance when training with multi-GPUs.
```
git clone https://github.com/NVIDIA/nccl.git
cd nccl
make
make install
luarocks install nccl
```
set `LD_LIBRARY_PATH` in file `~/.bashrc` if `libnccl.so` is not found.
4. Prepare dataset.
Create a symbolic link to the images directory of the MPII dataset:
```
ln -s PATH_TO_MPII_IMAGES_DIR data/mpii/images
```
Create a symbolic link to the images directory of the LSP dataset (images are stored in `PATH_TO_LSP_DIR/images`):
```
ln -s PATH_TO_LSP_DIR data/lsp/lsp_dataset
```
Create a symbolic link to the images directory of the LSP extension dataset (images are stored in `PATH_TO_LSPEXT_DIR/images`):
```
ln -s PATH_TO_LSPEXT_DIR data/lsp/lspet_dataset
```
## Training and Testing
### Quick Start
#### Testing from our pretrained model
Download our pretrained model to `./pretrained` folder from [Google Drive](https://drive.google.com/drive/folders/0B63t5HSgY4SQbmJoRV9fOTBFTlU?resourcekey=0-AT4Eik8SdlpF2nQ7vevbIg&usp=sharing). Test on the MPII validation set by running the following command
```
qlua main.lua -batchSize 1 -nGPU 1 -nStack 8 -minusMean true -loadModel pretrained/model_250.t7 -testOnly true -debug true
```
For multi-scale testing, run
```
qlua evalPyra.lua -batchSize 1 -nGPU 1 -nStack 8 -minusMean true -loadModel pretrained/model_250.t7 -testOnly true -debug true
```
**Note**:
* If you DO NOT want to visualize the training results. Set `-debug false` and use `th` instead of `qlua`.
* you may set the number of scales in `evalPyra.lua` (Line 22 ). Use fewer number of scales or multiple GPUs if "out of memory" occurs.
* use `-loadModel MODEL_PATH` to load a specific model for testing or training
#### Train a two-stack hourglass model
Train an example two-stack hourglass model on the MPII dataset with the proposed *Pyramids Residual Modules* (PRMs)
```
sh ./experiments/mpii/hg-prm-stack2.sh
```
### Customize your own training and testing procedure
A sample script for training on the MPII dataset with 8-stack hourglass model.
```bash
#!/usr/bin/env sh
expID=mpii/mpii_hg8 # snapshots and log file will save in checkpoints/$expID
dataset=mpii # mpii | mpii-lsp | lsp |
gpuID=0,1 # GPUs visible to program
nGPU=2 # how many GPUs will be used to train the model
batchSize=16
LR=6.7e-4
netType=hg-prm # network architecture
nStack=2
nResidual=1
nThreads=4 # how many threads will be used to load data
minusMean=true
nClasses=16
nEpochs=200
snapshot=10 # save models for every $snapshot
OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=$gpuID th main.lua \
-dataset $dataset \
-expID $expID \
-batchSize $batchSize \
-nGPU $nGPU \
-LR $LR \
-momentum 0.0 \
-weightDecay 0.0 \
-netType $netType \
-nStack $nStack \
-nResidual $nResidual \
-nThreads $nThreads \
-minusMean $minusMean \
-nClasses $nClasses \
-nEpochs $nEpochs \
-snapshot $snapshot \
# -resume checkpoints/$expID \ # uncomment this line to resume training
# -testOnly true \ # uncomment this line to test on validation data
# -testRelease true \ # uncomment this line to test on test data (MPII dataset)
```
## Evaluation
You may evaluate the PCKh score of your model on the MPII validation set. To get start, download our prediction `pred_multiscale_250.h5` to `./pretrained` from [Google Drive](https://drive.google.com/drive/folders/0B63t5HSgY4SQbmJoRV9fOTBFTlU?resourcekey=0-AT4Eik8SdlpF2nQ7vevbIg&usp=sharing), and run the MATLAB script `evaluation/eval_PCKh.m`. You'll get the following results
```
Head , Shoulder , Elbow , Wrist , Hip , Knee , Ankle , Mean ,
name , 97.41 , 96.16 , 91.10 , 86.88 , 90.05 , 86.00 , 83.89 , 90.27
```
## Citation
If you find this code useful in your research, please consider citing:
```
@inproceedings{yang2017pyramid,
Title = {Learning Feature Pyramids for Human Pose Estimation},
Author = {Yang, Wei and Li, Shuang and Ouyang, Wanli and Li, Hongsheng and Wang, Xiaogang},
Booktitle = {arXiv preprint arXiv:1708.01101},
Year = {2017}
}
```